Daron Acemoğlu

Daron Acemoglu (born 3 September 1967) is a Turkish-American economist and Institute Professor at the Massachusetts Institute of Technology (MIT). His research covers political economy, economic development and growth, labor economics, inequality, networks, and technological change. In 2024, he shared the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel with Simon Johnson and James A. Robinson "for studies of how institutions are formed and affect prosperity." His work on technology uses task-based models to examine how automation and the creation of new tasks affect productivity, wages, and employment. ^[1][2][3]

Education and academic career

Acemoglu was born in Istanbul. He earned a BA from the University of York in 1989, an MSc from the London School of Economics in 1990, and a PhD from the London School of Economics in 1992. His doctoral thesis was titled Essays in Microfoundations of Macroeconomics: Contracts and Economic Performance. His curriculum vitae lists both United States and Turkish nationality. ^[1][2][3]

He was a lecturer at the London School of Economics in 1992-1993 and joined MIT as an assistant professor in 1993. He became the Pentti Kouri Associate Professor in 1997 and a full professor in 2000. He held the Charles P. Kindleberger Professorship from 2004 to 2010 and the Elizabeth and James Killian Professorship from 2010 to 2019. MIT appointed him an Institute Professor in 2019. ^[2]

Acemoglu's books include Economic Origins of Dictatorship and Democracy (2006, with Robinson), Introduction to Modern Economic Growth (2009), Why Nations Fail (2012, with Robinson), The Narrow Corridor (2019, with Robinson), and Power and Progress (2023, with Johnson). ^[1][2][12]

Institutions and economic development

Much of Acemoglu's research asks how political institutions, economic institutions, and the distribution of political power interact over time. The scientific background to the 2024 economics prize describes a chain in which political institutions influence the distribution of power, which shapes economic institutions and, in turn, economic outcomes. It also emphasizes commitment problems: groups that hold power may be unable to make credible promises about how they will use it later. ^[4]

In "The Colonial Origins of Comparative Development" (2001), Acemoglu, Johnson, and Robinson used historical estimates of European settler mortality as an instrument for present-day institutions. Their proposed mechanism was that Europeans were more likely to establish settlement institutions where mortality was lower and more extractive institutions where settlement was difficult. They reported large estimated effects of institutions on income per capita. Their follow-up paper "Reversal of Fortune" (2002) documented that, among regions colonized by European powers, places that were relatively prosperous around 1500 often became relatively poor, and argued that institutional changes help explain the reversal. ^[5][6]

The settler-mortality research design has also been debated. In a 2012 American Economic Review comment, David Albouy challenged the comparability and construction of parts of the mortality data and argued that the estimates were not robust. Acemoglu, Johnson, and Robinson replied in the same issue that Albouy's exclusions and coding choices were unwarranted and that their results remained robust under corrected data and alternative specifications. ^[7][8]

Acemoglu and Robinson later examined the relationship between political regimes and economic performance. A 2019 study with Suresh Naidu and Pascual Restrepo estimated that transitions to democracy increase GDP per capita by about 20 percent in the long run. This is an estimate from their panel-data design, not a claim that every democratization produces the same outcome. ^[9]

Tasks, automation, and inequality

Acemoglu and Restrepo model production as a set of tasks that can be assigned to labor or capital. In their framework, automation creates a displacement effect by moving tasks from workers to machines. New labor-intensive tasks can create a reinstatement effect by expanding the work in which labor has a comparative advantage. Productivity improvements can offset displacement, but automation does not necessarily produce large productivity gains. Acemoglu has described some automation applications as "so-so technologies": systems that displace workers while creating only modest productivity gains. ^[10][15]

Their empirical work applies this framework to labor-market data. "Robots and Jobs," published in the Journal of Political Economy in 2020, found negative effects of greater exposure to industrial robots on employment and wages across United States commuting zones. The paper distinguishes these local effects from aggregate effects, which also depend on trade between regions and other general-equilibrium adjustments. ^[11]

In a 2022 Econometrica article, Acemoglu and Restrepo reported that 50 to 70 percent of changes in the United States wage structure over the previous four decades were accounted for, in their analysis, by relative wage declines among groups specializing in routine tasks in rapidly automating industries. The result is an estimate produced by their task-displacement measures and model, not a direct measurement of automation's share of all inequality. ^[13]

Artificial intelligence and the economy

Acemoglu extends the task-based approach to artificial intelligence, including generative AI and large language models. His argument focuses on the direction of innovation: an AI system may automate an existing task, improve a worker's productivity in a task, or create a new task. These uses can have different effects on labor demand even when they rely on similar technical capabilities. In Power and Progress, Acemoglu and Johnson argue that technology's benefits depend on social and institutional choices about how it is developed and deployed. ^{[10][12][14][15]}

Macroeconomic estimates

In "The Simple Macroeconomics of AI," first issued as an NBER working paper in May 2024 and published in Economic Policy in 2025, Acemoglu estimated the effect of then-available AI advances over a ten-year horizon. His baseline calculation assumed that approximately 20 percent of United States labor tasks were exposed to AI. It used a separate study's estimate that 23 percent of computer-vision-exposed tasks could be profitably automated within ten years, then extrapolated that share to all exposed tasks. The paper took average labor-cost savings on affected tasks to be 27 percent and converted this to an average task-level overall cost saving of 14.4 percent after weighting by industry labor shares. ^[14]

The estimates depend on how difficult tasks and investment responses are treated:

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These are model-based estimates conditional on the paper's exposure, adoption, cost-saving, and investment assumptions. They are not observed outcomes or an upper bound on every future form of AI. The paper also found no evidence in its quantitative exercise that AI would reduce labor-income inequality, and it predicted a wider gap between capital and labor income. Acemoglu has argued that AI could produce larger and more broadly shared gains if development shifted toward providing information and expertise that complements workers. ^[14][15]

Pro-worker AI and knowledge models

In a February 2026 NBER working paper, Acemoglu, David Autor, and Johnson classified technological change as labor-augmenting, capital-augmenting, automating, expertise-leveling, or new-task-creating. Within their framework, only the creation of new tasks is unambiguously "pro-worker" because it generates demand for new human expertise. The authors proposed policy directions involving public investment, taxation, competition, and intellectual-property rules. The paper is a policy framework circulated for discussion, not a peer-reviewed evaluation of enacted policies. ^[16]

Two 2026 theoretical papers studied how AI-mediated information can affect collective knowledge. Acemoglu, Dingwen Kong, and Asuman Ozdaglar built a dynamic model in which sufficiently accurate agentic recommendations can improve current decisions while weakening incentives for human learning. Under specified parameter conditions, the model can converge to a state in which shared general knowledge disappears. In a separate working paper, Acemoglu, Tianyi Lin, Ozdaglar, and James Siderius extended a DeGroot social-learning model with an AI aggregator. Their results distinguish rapidly updating global aggregation, which can reinforce distorted beliefs in some modeled environments, from local aggregation, which performs better under the paper's robustness criterion. Both are theoretical results; neither paper establishes that knowledge collapse has occurred in real-world AI use. ^[17][18]

Recognition

The American Economic Association awarded Acemoglu the John Bates Clark Medal in 2005. The award recognized his work across political economy, labor economics, development, and macroeconomics. ^[19] In 2024, Acemoglu received a one-third share of the economics prize for research on institutions and prosperity. ^[3]

References

1. ^MIT Department of Economics. "Daron Acemoglu." economics.mit.edu/...daron-acemoglu
2. ^Acemoglu, Daron. "Curriculum Vitae." MIT Department of Economics, 3 October 2024. economics.mit.edu/...CV%20October%203%202024.pdf
3. ^Nobel Prize Outreach. "The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2024: Facts." nobelprize.org/...facts
4. ^Committee for the Prize in Economic Sciences in Memory of Alfred Nobel. "Institutions and Prosperity: Scientific Background to the Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel 2024." nobelprize.org/...ed-economicsciencesprize2024.pdf
5. ^Acemoglu, Daron, Simon Johnson, and James A. Robinson. "The Colonial Origins of Comparative Development: An Empirical Investigation." American Economic Review 91, no. 5 (2001): 1369-1401. aeaweb.org/articles
6. ^Acemoglu, Daron, Simon Johnson, and James A. Robinson. "Reversal of Fortune: Geography and Institutions in the Making of the Modern World Income Distribution." Quarterly Journal of Economics 117, no. 4 (2002): 1231-1294. nber.org/...w8460
7. ^Albouy, David Y. "The Colonial Origins of Comparative Development: An Empirical Investigation: Comment." American Economic Review 102, no. 6 (2012): 3059-3076. aeaweb.org/articles
8. ^Acemoglu, Daron, Simon Johnson, and James A. Robinson. "The Colonial Origins of Comparative Development: An Empirical Investigation: Reply." American Economic Review 102, no. 6 (2012): 3077-3110. aeaweb.org/articles
9. ^Acemoglu, Daron, Suresh Naidu, Pascual Restrepo, and James A. Robinson. "Democracy Does Cause Growth." Journal of Political Economy 127, no. 1 (2019): 47-100. nber.org/...w20004
10. ^Acemoglu, Daron, and Pascual Restrepo. "Automation and New Tasks: How Technology Displaces and Reinstates Labor." Journal of Economic Perspectives 33, no. 2 (2019): 3-30. aeaweb.org/articles
11. ^Acemoglu, Daron, and Pascual Restrepo. "Robots and Jobs: Evidence from US Labor Markets." Journal of Political Economy 128, no. 6 (2020): 2188-2244. nber.org/...w23285
12. ^Acemoglu, Daron, and Simon Johnson. "Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity." PublicAffairs, 2023. shapingwork.mit.edu/power-and-progress
13. ^Acemoglu, Daron, and Pascual Restrepo. "Tasks, Automation, and the Rise in U.S. Wage Inequality." Econometrica 90, no. 5 (2022): 1973-2016. econometricsociety.org/...ecta200454.pdf
14. ^Acemoglu, Daron. "The Simple Macroeconomics of AI." NBER Working Paper 32487, May 2024; Economic Policy 40, no. 121 (2025): 13-58. nber.org/...w32487
15. ^MIT Department of Economics. "Daron Acemoglu: What do we know about the economics of AI?" 6 December 2024. economics.mit.edu/...do-we-know-about-economics-ai
16. ^Acemoglu, Daron, David Autor, and Simon Johnson. "Building Pro-Worker Artificial Intelligence." NBER Working Paper 34854, February 2026. nber.org/...w34854
17. ^Acemoglu, Daron, Dingwen Kong, and Asuman Ozdaglar. "AI, Human Cognition and Knowledge Collapse." NBER Working Paper 34910, February 2026. nber.org/...w34910
18. ^Acemoglu, Daron, Tianyi Lin, Asuman Ozdaglar, and James Siderius. "How AI Aggregation Affects Knowledge." Working paper, 25 March 2026. economics.mit.edu/...AI_aggregation.pdf
19. ^American Economic Association. "Daron Acemoglu, Clark Medalist 2005." aeaweb.org/...daron-acemoglu